There is a widening gap between demand for data transmitted via computer networks and available bandwidth. Similarly, there is a great demand for the transmission of data over digital radio links, for example to and from mobile radios and mobile ‘phones. This has fueled the search for improved methods of data compression and more efficient transmission technologies.
Prior art transmission technologies that are particularly suited for video applications focus on interpreting image data at the transmission source, transmitting the interpretation data rather than the image itself, and using the interpretation data at the destination. The interpretation data may or may not be transmitted in compressed form.
Two alternative approaches to image interpretation are the ‘image-driven’, or bottom-up, approach, and the ‘model-driven’, or top-down, approach.
The image-driven approach relies on features in the image, such as edges or corners, to propagate “naturally” and form meaningful descriptions or models of image content. A typical example is figure-ground image segmentation, where the task is to separate the object of interest in the foreground from the background.
In the model-driven approach, information regarding content expectation is used to extract meaning from images. A typical example is object recognition where an outline Computer-Aided Design (CAD) model is compared to edges found in the image—an approach commonly used in manufacturing line inspection applications.
The key difference between the image driven and model driven approaches is in the feature grouping stage. In the image-driven approach, the cues for feature grouping come from the image, whereas in the model-driven approach the cues come from the comparison models.
In one variation of an image-driven approach, a number of small salient patches or ‘icons’ are identified within an image. These icons represent descriptors of areas of interest. In this approach saliency is defined in terms of local signal complexity or unpredictability, or, more specifically, the entropy of local attributes. Icons with a high signal complexity have a flatter intensity distribution, and, hence, a higher entropy. In more general terms, it is the high complexity of any suitable descriptor that may be used as a measure of local saliency.
Known salient icon selection techniques measure the saliency of icons at the same scale across the entire image. The scale to use for selection across the whole image may be chosen in several ways. Typically, the smallest scale at which a maximum occurs in the average global entropy is chosen. However, the size of image features varies. Therefore a scale of analysis that is optimal for a given feature of a given size might not be optimal for a feature of a different size.
There is therefore a need to further improve salient icon selection techniques.